You re In then You re Out: The Incidence and Effects of Being Outsourced

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1 You re In then You re Out: The Incidence and Effects of Being Outsourced Deborah Goldschmidt Johannes F. Schmieder Boston University Boston University NBER, and IZA November 2014 Abstract The nature of the relationship between employers and employees has been changing over the last few decades, with firms increasingly relying on contractors, temp agencies and franchises rather than hiring employees directly. We investigate the impact of this transformation on the wage structure by following jobs that are being moved outside of the boundary of lead employers to contracting firms. For this end we develop a new method for identifying outsourcing of janitorial, security and food services in administrative data using the universe of social security records in Germany. We first document a dramatic growth of outsourcing in Germany over the last three decades. Event-study analysis joint with propensity score matching show that wages in outsourced jobs fall by approximately 10-15% relative to similar jobs that are not outsourced. We find evidence that the wage losses associated with outsourcing come from a loss of firm-specific rents, suggesting that labor cost savings are an important reason why firms choose to contract out these services. Finally, we tie the increase in outsourcing activity to broader changes in the German wage structure, particularly showing how outsourcing contributed to the increase in wage dispersion and the strength of assortative matching between workers and firms. We would like to thank David Autor, David Card, Christian Dustmann, Robert Gibbons, Pat Kline, Hillary Hoynes, Kevin Lang, Mark McCabe, Claudia Olivetti, Jim Rebitzer, David Weil, Birger Wernerfelt, Heidi Williams, and Seminar Participants at Boston University and UC Berkeley. All errors are our own.

2 1 Introduction Wage discrimination is rarely seen in large firms despite the benefits it could confer. As long as workers are under one roof, the problems presented by horizontal and vertical equity remain. But what if the large employer could wage discriminate by changing the boundary of the firm? - Weil (2014) - The last decades have seen a thorough transformation of the nature of the labor market, where large firms rely increasingly on non-traditional employment arrangements such as outsourcing, temporary or contingent work, offshoring and subcontracting. Across a wide range of industries, firms have been focusing on their core competencies and hiring outside companies to provide services which were once performed by their own employees, such as cleaning, security, logistics, human resources and IT. Such outsourcing to business service providers potentially allows for reductions in wages for the contracted-out jobs. The outsourcing firms are often traditional lead companies in sectors such as manufacturing or finance, and typically offer the most attractive jobs, with higher wages, increased job security, strong worker representation, and union coverage. Factors such as collective bargaining agreements (Card, Lemieux, Riddell 2004, DiNardo and Lee 2004) or efficiency wage considerations linked to fairness perception (Akerlof and Yellen 1990) may lead to wage compression within firms and rent sharing of firm profits, which in turn pushes up wages for workers who would otherwise have lower paying outside job opportunities. Large employers may then find it beneficial to reduce the number of direct employees who benefit from such wage premia by outsourcing jobs to sub-contractors. These business services firms compete fiercely with each other for service contracts from large companies on price, and since labor costs are a large share of business services firms total costs, this creates sharp pressure to lower wages and reduce benefits. Furthermore workers in these firms likely benefit less from collective bargaining agreements and protection from unions, since they would typically not be covered by the same sectoral union of the outsourcing company. Even though anecdotal and qualitative evidence for these changes in the labor market abound, the research in the economics litera- 1

3 ture analyzing this phenomenon is quite limited. 1 One problem with analyzing outsourcing is that it is very difficult to measure and can usually only be approximated using industry and occupation codes. Furthermore, even with such an approximation, the existing research has relied largely on cross-sectional datasets on the worker level with almost no information on the outsourcing firms and limited information on the actual jobs people do. Outsourcing however inherently occurs on the job level, where certain tasks or inputs are moved out of the firm and provided externally. Since jobs are typically not directly observed, it is difficult to identify the true causal impact of outsourcing on wages. In this paper we analyze the phenomenon of domestic labor service outsourcing in Germany using detailed administrative data on the universe of workers and firms. 2 We document for the first time in detail the rise of outsourcing of labor services over the last three decades in Germany, focusing in particular on cleaning, security and food services. We develop a new method for identifying outsourcing events at the time that they occur, which allows us to observe wages for a particular job before and after the job is outsourced. Based on this we provide credible estimates of the causal effect of outsourcing on wages, documenting that moving jobs outside the boundary of the firm leads to large wage reductions. Next, we investigate in detail whether the wage reductions we find after outsourcing can be explained with the loss of firm wage premia and whether it is plausible that at least part of the reason that firms outsource is that it allows them to avoid paying such rents. Finally, we consider the relationship between the documented increases and impacts of outsourcing and the broad changes in the wage distribution experienced by Germany over the last decades. An important methodological innovation for this project is the development of a new method of identifying a particular type of outsourcing which we refer to as in-house out- 1 David Weil (2014) provides a detailed, largely qualitative, analysis of the practice of domestic outsourcing and an overview of the quantitative research in economics. He only lists two papers that estimate wage differentials between contracted-out and in-house workers based on CPS data (discussed below) and only a handful of studies based on firm surveys that measure the increase in the incidence of sub-contracting of labor services. 2 We use the term domestic outsourcing in order to differentiate it from offshoring, which is a form of outsourcing that has been studied much more widely in the economics literature even though it is not clear that it is quantitatively more important. 2

4 sourcing. This type of outsourcing refers to situations where large employers spin out a group of workers providing a particular service, such as cafeteria workers, to a legally separate business unit, for example a subsidiary or an existing business service provider. In these situations the outsourced workers still work together and do essentially the same job at the same physical location, but under a different employer. We show that such outsourcing events can be identified in administrative datasets using worker flows between establishments. The basic intuition is that if a group of workers is contracted out at the same time, this can be observed by following the establishment identifiers as well as occupation and industry codes. For example, if we observe a group of workers splitting off from a large bank in year t-1 and forming a new establishment identifier in year t with an industry code of cafeteria, this is likely reflecting that the bank is outsourcing its cafeteria. This is further supported if the workers who are leaving worked in food related jobs in year t-1 at the bank, and the bank does not replace these occupations in the following year. These instances of in-house outsourcing likely only constitute a small share of all outsourcing, for example missing outsourcing events where all workers providing a particular task are simply laid off and the task is sub-contracted to an external provider with different employees. However, in-house outsourcing events provide a particularly powerful testing ground to analyze the wage effects of outsourcing, since we are essentially following jobs over time where both the worker and the work location remain the same, so that effects on wages can be attributed directly to the change in the employment relationship without selection or omitted variable bias. We complement this analysis with a broader measure of outsourcing, where a worker in a food, cleaning or security occupation is defined as outsourced if he is employed by a business services firm. Using both measures we find a dramatic increase in outsourcing in Germany that has accelerated in the late 1990s and continues into recent years. 3 3 This trend to vertical disintegration appears to be more widespread than just for the area of labor services. E.g. Dustmann et al. (2014) document that final goods producers in the German manufacturing sector have been relying increasingly on buying intermediate inputs from outside the firm and from abroad (offshoring) and are responsible for a increasingly smaller share of the value added of final goods. 3

5 Our main contribution is to provide cleanly identified effects of outsourcing on the wages paid for outsourced jobs. We show that workers who are outsourced in in-house outsourcing events typically stay with the business service firm they are outsourced to for the following years, and their employment is similarly stable as for workers in the same occupations and industries who are not outsourced. This allows us to interpret the wage effects of outsourcing as the effect on the job level, free of selection. Compared to the control group, wages of outsourced jobs drop sharply, falling by around 6 percent over the first 1-5 years after being outsourced, and by around 12 percent in the long run, i.e. about 5-10 years. As an alternative method of estimating the wage losses from outsourcing, we compare wages of workers in cleaning, food and security occupations who are employed in business services firms with those employed directly by other employers. 4 Using this method, we also find wage differences of around 10 percent, even after controlling for individual fixed effects in order to account for selection between workers into the two different establishment types. Firms may choose to engage in these types of alternative employment arrangements for various reasons. Subcontractors can provide increased flexibility for firms whose needs vary throughout the year, or provide specialized skills or technology that would be costly for a firm to invest in. Outsourcing can also provide cost savings through lower labor costs, if outsourced workers are excluded from wage premia or rents at the outsourcing firm. In order to test the hypothesis that the wage losses of outsourced workers stem from being excluded from firm rents, we analyze whether outsourcing is associated with changes in firm characteristics typically associated with firm rents, such as firm size and average pay of coworkers. In addition, we estimate a full wage model with worker and establishment fixed effects using the universe of wage records in Germany (as in Card, Heining and Kline henceforth CHK - and in the spirit of Abowd, Kramarz and Margolis 1999) and use the 4 This is the same method used by Abraham (1990) and Dube and Kaplan (2010), who use CPS data to estimate the effect of outsourcing on wages. It is also similar to the earlier literature that estimated industry wage differentials using individual fixed effects, e.g. Krueger and Summers (1988). The criticism of this approach in Gibbons and Katz (1992) applies in the outsourcing case as well, which is why identifying in-house outsourcing as an exogenous (from the individual s perspective) shock is crucial. 4

6 estimated establishment fixed effects (AKM effects) as a measure of the firm wage premium. We find that all three characteristics drop substantially at the time of outsourcing; notably, the AKM effect drops by 10 log points at the time of outsourcing, explaining essentially the entire wage loss. Furthermore, we show that wage losses are highly correlated with measures of wage premia at the outsourcing establishment and are much larger when workers are outsourced from large employers or establishments with high AKM effects. Finally we document that establishments that appear to be paying above market wages are more likely to outsource parts of their labor force. These findings suggest that exclusion from establishment wage premia is a driving factor for the wage losses and likely part of the motivation for why firms outsource, though certainly not the only reason. Germany provides a particularly interesting setting to study outsourcing. Over the last few decades there has been a substantial increase in wage inequality, with significant wage cuts at the bottom of the wage distribution (Dustmann, Ludsteck and Schönberg 2009; CHK). These changes in the wage structure are in part explained by de-unionization, the erosion of the sectoral level collective bargaining system, and the increased decentralization of the wage setting mechanism. 5 However, as CHK show, a significant portion of the rise in wage inequality comes from increased assortative matching of workers employed together with others in the same or similar jobs, and low skilled workers being matched with low paying firms. Increased reliance on outsourcing, particularly of lower-skilled labor services and other inputs, provides a natural explanation for this change, as lead firms move parts of their labor inputs out of the core workforce and into highly specialized, lower-paying business service firms. Outsourcing can thus shed some light on how these changes in the German wage structure came about. Furthermore, it may also explain why unit labor costs in the manufacturing sector declined sharply even though manufacturing wages remained relatively stable (See Dustmann et al. 2014): while large employers continue to pay relatively high wages, they benefit from the sharp drop in wages at their sub-contractors and suppliers. 5 See for example Dustmann, et al. (2014) for a discussion of how the German reunification in combination with the Eastern EU expansion lead to the reduction in collective bargaining coverage rates. 5

7 While we view outsourcing as a complementary explanation to de-unionization for the change in the German wage structure and the increases in competitiveness, we also believe that these two channels are likely closely intertwined, since on the one hand weaker unions facilitated outsourcing decisions and, on the other hand, outsourcing weakened the bargaining positions of unions and work councils. The next section presents the data and institutional background, as well as a description of our measures of domestic outsourcing. Section 3 presents our empirical results on the effects of outsourcing on workers employment trajectories and wages of outsourced jobs. In section 4 we investigate the connection between the wage losses associated with outsourcing and firm rents. Section 5 relates outsourcing to the broader changes observed in the German labor market and wage distribution. Section 6 concludes. 2 Measuring Outsourcing 2.1 Institutional Background Throughout the 1970s and 1980s, while union coverage declined in many other countries, Germany maintained high union density and relatively stable collective bargaining agreements, covering around percent (see Doellgast and Greer 2007, Fitzenberger, Kohn, and Lembcke 2013, Dustmann et al. 2014). Germany features a somewhat unique collective bargaining system, the so called dual system, where wages are negotiated between employer associations and unions on the industry level or on the firm level, often in close coordination with elected firm or establishment level work councils. The close level of cooperation between the different parties appeared to lead to relatively high wages and good working conditions, while at the same time avoiding costly strikes and conflicts between unions and employers. However, the system was always based on contractual relations and mutual agreements and firms were free to leave the collective agreements and instead set wages either in firm level negotiations or without any agreement. Firms who do not leave the union contracts can achieve additional wage flexibility through so called opening clauses, which allowed for pay- 6

8 ing wages below the collectively agreed upon level. While workers can attempt to fight an employer who tries to leave the collective agreements through strikes or the work councils, the success of this will depend on the ability of the employer to threaten job cuts or even plant closings to move production elsewhere. Starting in the early 1990s, Germany experienced a sharp decline in collective bargaining coverage rates and union membership as more and more firms opted out of the industry level agreements and either did not have any agreements or any firm level agreements. 6 Many existing firms left the employer associations while new firms opted to not join them in the first place (see CHK). Dustmann et al. (2009) and CHK argue that this decline was kickstarted by the decision of labor unions to impose West German wage levels in East German establishments almost immediately after the reunification. The large productivity gap essentially forced East German employers to leave the collective agreements, which in turn led to firms in West Germany imitating them and leaving the agreements as well. The increased pressure from globalization, the real threat of offshoring production to Eastern Germany or the newly accessible Eastern European countries, and the high levels of unemployment in Germany, all provided West German firms with the necessary leverage to force work councils and unions to agree to these changes. While work councils have to be consulted for a wide variety of firm level decisions that affect workers, this does not apply to outsourcing decisions and German firms are legally free to do so at their discretion. In practice work councils and unions may try to fight outsourcing, but the success will depend on the willingness of the core workforce to stand up for the workers affected by outsourcing. It seems likely that the same factors that led to decreased union coverage likely also facilitated outsourcing of parts of the workforce. On the other hand, as noted by Doellgast and Greer (2007), outsourcing itself offers a way for firms to sidestep the unions, since even if a firm is following a collective bargaining agreement, outsourced workers employed by a different sector typically would not be covered by the 6 For example Dustmann et al. (2014) report that from 1995 to 2008, industry wide agreements fell from 75 to 56 percent, while firm level agreements stayed close to 10 percent. 7

9 same agreement. Furthermore workers in business service firms are often not well organized and in many cases do not even form a work council. Another factor that has facilitated outsourcing in Germany over the last two decades has been a steady deregulation of the temp agency sector (Vitols 2004). The number of employees in this sector subsequently increased dramatically since the early 1990s and the sector became more established with many large temp agencies offering their services to other firms, thus making it easier to outsource.data For this project we use the Integrated Employment Biographies data (IEB) which represents the universe of social security records in Germany over the time period 1975 to The IEB has been made available through the Research Data Center of the German Federal Employment Agency at the Institute for Employment Research (IAB). Employers are required to file a report for all employees who are employed during a year. This report contains information on the duration of employment, the total pay over that period, the employment type (full-time, part-time, apprentice), and a number of demographic variables (such as education, nationality, gender, and age). The pay information is generally very accurate (since it determines the social security contributions) but top coded. Furthermore the IEB contains information on benefits receipt from the unemployment insurance system. The data covers all employment subject to social security contributions, but excludes certain types of government employees and the self-employed. For our approach of measuring outsourcing it is particularly important that for every worker there is information on industry and occupation available. Furthermore, since employers and individuals are uniquely identified through establishment and person IDs, it is possible to construct complete employment histories for individual workers, and to follow establishments over time. One limitation is that the data only contains establishment, not firm identifiers. Multi-establishment firms typically have a separate identifier for each establishment they own, or they may combine several establishments within the same county 7 See Oberschachtsiek et al. (2009) 8

10 (such as branches) under a single establishment identifier, but establishment identifiers do not span across multiple counties (See Hethey-Maier and Schmieder 2013). 2.2 In-House Outsourcing Although the IEB, like most data sets, does not contain any specific information on outsourcing, we developed a method to identify a particular type of outsourcing using worker flows between establishments. We call this in-house outsourcing, and it refers to cases where companies contract out part of their workforce to an independent sub-contractor but where the same employees continue their work at the same physical location. For example, in 2005 the Daimler corporation implemented a large cost-saving program called CORE to focus on its core business competencies. As part of this program it outsourced several of its in-house cafeterias into a legally independent subsidiary company, which was at first fully owned by Daimler and later sold in parts to various business service firms. The employees largely remained the same and still worked at the same locations, but were now employed by a different employer. 8 As we argue below, since in the case of in-house outsourcing the worker and workplace remain the same with the main change being the change in the employment contract, this allows for particularly clean estimates of the effects of outsourcing on jobs. We identify these in-house outsourcing events using worker flows between establishment identifiers, implementing a strategy similar to Hethey-Maier and Schmieder (2013) which dealt with classifying establishment entries and exits. Starting with the full universe of covered workers as of June 30 in each year from 1975 to 2009, we track workers as they move between establishments from year to year. We define a cluster of workers to be a group of workers who were all employed in establishment A in one year and then, in the following year, were all employed in establishment B; a cluster represents an outflow from establishment A, the predecessor, and an inflow into establishment B, the successor. We create a data set of 8 This description of the events is based on personal conversations with Daimler employees. There are many other case studies describing similar events, e.g. Doellgast and Greer (2007) describe outsourcing in the automobile and telecommunications sector in Germany, Dietz, Hall and Jacobs (2013) describe outsourcing of airport workers in the U.S., and Smith Institute (2014) provides several examples from the UK. 9

11 all such clusters between every pair of establishments in each year. In-house outsourcing events are defined using these clustered flows between predecessor and successor establishments. A clustered flow at time t is considered an outsourcing event if the following conditions hold: First, the flow must consist of 10 employees or more, to eliminate small flows that may be a part of regular year-to-year worker movements. The predecessor establishment must have at least 50 employees in the year prior to the flow, continues to exist in the following year and does not shrink by more than 50%, to ensure that the flow we observe is not due to an establishment closing, downsizing sharply or breaking apart. The flow must also represent less than 30% of employment in the predecessor in the previous year, so that we are certain that the outsourced employees represent only a small part of the predecessor s business. If the successor is a new establishment (i.e. the establishment ID appears in the data for the first time in year t), then we further require that the clustered flow makes up 65% or more of the successor s employment. Finally, we restrict the successor establishment to have an industry code corresponding to a business service firm in either food services, cleaning or security, and ensure that the predecessor establishment is not a business service firm, giving us further confidence that these flows are likely to be outsourcing occurrences and not spin-offs or other types of establishment changes. 9 For all outsourcing events, we call the predecessor establishment the mother, and the successor establishment the daughter. 10 Columns 1 and 2 of Table 1 show characteristics of Outsourcing (OS) and Non-outsourcing 9 The Online Appendix lists the precise industry codes we use to define outsourcing and business service firms. 10 While the outsourcing definition that we use does not explicitly exclude situations where a mother establishment re-hires the types of workers who left the firm, we find that this is not typically the case. In Appendix Figure A-1 (a) we graph the number of workers employed in the outsourced occupation at the mother establishment (i.e. for establishments outsourcing cleaning tasks, this would be the number of workers who are in occupations labeled cleaner ) in the years surrounding outsourcing (which occurs between year -1 and 0). We find that this number drops sharply at the time of outsourcing and does not increase, indicating that these workers are not replaced. If our method were instead just capturing layoffs or quits of groups of workers while the corresponding tasks still stayed in-house, then the mother establishment would have to replace these workers with others in the same occupation. Appendix Figure A-1 (b) shows establishment size before and after outsourcing, and while establishment size decreases slightly in the years before outsourcing, there is only a small drop at the time of outsourcing and afterwards employment continues to be relatively flat, assuring us that we are not capturing mass layoffs or other types of restructuring or downsizing. 10

12 (non-os) establishments as of the year For the first column, we include any established that outsourced part of its workforce in an in-house outsourcing event between 2000 and Column 2 includes all establishments who did not outsource during these years. Outsourcing establishments tend to be larger and older. They are also more likely to be in the retail, services or health industry sector. In fact, among outsourcing establishments the most common industries are department stores and hospitals, but there are also a sizable number of manufacturing plants, financial sector companies and transportation companies. While this type of outsourcing was relatively uncommon in the late 1970s and 1980s, the mid-90s saw a large increase in the number of outsourcing events to about per year, as can be seen in Figure 1 (a). This increase occurred across all three types of outsourcing events, which follow very similar time paths (Figure 1 (b)). The spikes in 1983 and 1988 in outsourcing of food services are all due to department stores outsourcing restaurants in those two years. We cannot link up our data to the company level across different counties, but it seems likely that in each of these years a large department store chain decided to outsource all of their restaurants simultaneously. We base this interpretation largely on the fact that the spikes are driven by outsourcing events with exactly the same industry codes of mothers and daughters, as well as similar establishment sizes in those years, while in other years there is a wide mix of industry codes among the different mother establishments. 2.3 Measuring Outsourcing using Industry and Occupation Codes While our method for identifying in-house outsourcing has the advantage that we can observe the event of outsourcing right when it happens, the disadvantage is that we are likely missing many instances where outsourced workers are not moved together to a separate business unit, or when outsourcing happens more gradually. For example we would not be capturing slower movements of tasks to outside contractor that are not at the extensive margin (getting rid of workers of a specific task or spinning off entire units of workers) and changes due to reallocation of employment shares among existing firms or between exiting and new firms 11

13 (who may for example rely more on outsourcing). This is, on the one hand, because the inhouse measure of outsourcing relies on worker flows that are somewhat extreme and therefore easily interpretable but also exclude many gray cases. On the other hand, this is because in-house outsourcing represents a flow measure (new outsourcing events) as opposed to a stock measure of the total amount of outsourcing in the labor market. In order to obtain a very broad picture of the level of sub-contracting of labor services, we use the method introduced by Abraham (1990), which relies on using occupation and industry codes to measure the share of workers in cleaning, food and security occupations who are outsourced. 11 This method defines outsourcing based on workers occupation and industry codes. Food services, cleaning, and security workers are identified by their 3-digit occupation codes. Outsourced workers are those who are employed at business service firms, i.e. establishments whose main business is providing services to other firms, identified by their industry codes. 12 For example, a food services worker such as a waiter or cook is considered to be an outsourced worker if she is employed in the catering or canteen industry. 13 In general one would expect that workers employed by business service firms are differently selected than workers who are employed directly by parent companies. Furthermore workers who are working for business service firms may well be doing somewhat different tasks, e.g. being assigned to different customers who hire the business services. While this makes this method for identifying outsourcing somewhat less suited for studying the wage effects of outsourcing, we can still use it to obtain an alternative estimate of the impact of 11 This method was also used by Dube and Kaplan (2010). 12 We use the term business service firm here in a narrow sense for firms who provide food, cleaning or security services to other firms. Firms who provide intermediate inputs or other services (such as IT or legal services) may in principle also constitute a form of outsourcing, but in this case the fact that a cleaner was outsourced seems more like a secondary effect. Here we focus on the specific event of outsourcing of cleaning (or food/security) tasks to cleaning (food/security) business service firms. 13 Business service industries for food occupations include canteens and catering. For cleaning, industries include industrial cleaning, cleaning of buildings, rooms and equipment, street cleaning, chimney-sweeping, and scaffolding and facade cleaning. For security occupations, the industries used were labeled security activities and security and storage activities. For a complete listing of industry and occupation codes used, see Online Appendix tables A-3 and A-4. 12

14 outsourcing on wages using the movement of food, cleaning, and security workers between outsourced and non-outsourced jobs, something we will get back to below. On the other hand, the advantage of this method is that it captures any type of subcontracting of janitorial, security or food services, whether a firm lets go of all of its service workers at once, lays them off slowly over time, or never employs these types of workers at all. This gives us a much broader view of the extent of outsourcing of these services in the economy, and the evolution over time. In Figure 2 we graph the share of workers in janitorial, security and food occupations who are outsourced - employed by business services firms - in each year. This share has increased substantially in all three groups over time. The most dramatic increase is the rise of cleaners working for firms providing cleaning services: while in 1975 only about 10 percent of cleaners were working for cleaning firms, this share has risen to almost 40 percent by Cleaning tasks may lend themselves particularly well to being broken out of the normal firm hierarchy and, as they are often very low-paying and may provide particularly good opportunities for cost savings through outsourcing. There was also a substantial rise of workers in security occupations who are working for business service firms, from less than 10 to almost 30 percent towards the end of the sample period. For food workers only the industry codes from 1999 onwards allow us to distinguish between business service firms and regular restaurants. Over the shorter time period there has been an increase in the share of food workers employed in business service firms, from about 16 percent to 26 percent. 14 This may still undercount the number of jobs outsourced in relation to establishment cafeterias, as establishment cafeterias are likely to also employ a large number of workers (such as cashiers) who are not in food occupations and who these figures thus do not count as outsourced. Another way to evaluate the extent of outsourcing of janitorial, security and food services is by analyzing industries which, although not in cleaning/security/food fields, typically 14 Food workers employed by restaurants and hotels are omitted from these calculations, as they would be considered neither outsourced nor in-house, but rather providing the main service of the establishment. We also exclude workers in the waiter, steward occupation who are employed in the air travel industry, as they are likely to be flight attendants and not relevant to this study. 13

15 employ some of these types of workers to provide services for their establishment or workforce. Here we focus on retail, manufacturing, finance and hospital industries. Figure 3 graphs the share of large firms (over 100 workers) in each of these industries employing at least one food, cleaning or security worker in each year. Starting with the top left graph, for the retail industry, we see that over time fewer retail establishments employed workers in these occupations. For example, in 1975, about 82% of retail establishments had at least one cleaning worker on staff, while in 2009, only about 20% did. Presumably these retail establishments are being cleaned somehow, and so it is likely that these tasks have been contracted out to another provider, rather than being done by workers employed directly by the retail firms. We see the same patterns among manufacturing and finance firms. For hospitals, the share employing food, security and cleaning workers has also decreased over time, although not quite as dramatically as in the other industries and mainly during the 1990s and 2000s. Both our measure of in-house outsourcing events as well as our analysis based on industry and occupation codes indicate that in Germany there has been an increase in outsourcing. These findings are consistent with evidence for the US and other countries For example, Abraham and Taylor (1996) used a survey question in the Industry Wage Surveys and found an increase in the fraction of work contracted out for janitorial, machine maintenance, engineering and drafting, accounting and computer tasks, while Wooden (1999) examined the AWIRS establishment survey and found evidence of a small increase in the use of contract workers in Australia from 1990 to Using the industry and occupation codes in the CPS from 1983 to 2000, Dube and Kaplan (2010) found an increase in the share of janitors and guards working for firms that provide labor services to other firms. Dey, Houseman and Polivka (2010) investigated industry and occupation codes in the Occupational Employment Statistics program and found that the share of workers in security, janitor, computer, and truck driver occupations employed in industries that provide services to other firms increased from

16 3 The Effects of Outsourcing on Jobs 3.1 Method Framework The fact that firms outsource jobs that fall outside of their core business function suggests that they are able to realize cost savings by doing so. Many of these are service jobs for which labor costs are a large share of inputs; therefore, one way to achieve cost savings would be through lower wages. It is, however, not immediately obvious why business service firms would pay lower wages than the outsourcing firm. In particular, if the labor market were perfectly competitive, then wages should simply be determined by the productivity of the worker and possibly a compensating wage differential component. Whether a particular job falls directly under a parent-business or is instead part of a subcontractor should not affect the wage in such an environment, and thus would not allow for wage savings by contracting out this task. However, if labor markets are not perfectly competitive, then outsourcing may allow for lower wages and thus labor costs savings by reducing the non-competitive wage component. In order to clarify this, consider the following simple wage setting equation : ln(w jt ) = f(e jt, Z jt, X i(j,t);t ) (1) where the (log) wage of job j at time t is some function of the employer of record, e jt, characteristics of the job or workplace Z jt, and individual characteristics X i(j,t);t of the worker i employed in the job. Note that i is a function of j and t, since the same job might be held by different people over time. The job is a set of tasks at a particular physical location, e.g. a cook in a cafeteria within a bank. The employer may either be the parent company operating the workplace, such as the bank, or a subcontractor that is hired by the parent company. Workplace or job characteristics that affect wages could include working 15

17 conditions or characteristics such as the amount of variety or stress involved in the required tasks. The identity of the employer may affect the wage paid for a job, separately from the characteristics of the workplace, for various reasons, such as if wages are set in a collective or individual wage bargaining setting or because of efficiency wage considerations. For example, if wages are set in a collective bargaining process, then the profits of the employer might affect individual wages through rent sharing. If the job is outsourced, then some or all of the rent component of the wage may be lost, either because the profits of the subcontractor may be lower (due to the more competitive environment) or because the workers may be in a weaker bargaining position, for example because they are not covered by the same labor union or they might find it harder to go on strike (since the subcontractor can simply be replaced). The effect of outsourcing could be estimated by: ln(w jt ) = δ1[e jt = contractor] + Z jt β + X i(j,t);t γ + ɛ i(j,t),t (2) where 1[e t = contractor] is an indicator function taking a value of one if the employer is a contractor and zero if the employer is a parent company. However, in practice employer status (in-house vs contractor) is likely correlated with workplace and individual worker characteristics. While panel data may help to some extent to control for individual characteristics, it is very rare to have information on job characteristics to satisfactorily deal with the omitted variable bias problem. Our in-house outsourcing definition, along with our research design and data, provide a straightforward solution to the omitted variable problem. In-house outsourcing identifies events where outsourced workers are likely to remain in the same workplace doing the same job but under a different contractual arrangement. In our empirical design, we follow workers before and after they are outsourced, comparing wages over time and controlling for individual fixed effects. We construct in-house outsourcing so that individuals are likely to 16

18 be working the same job in period t=-1 as in period t=0, only that the outsourced workers in t=0 are now employed by a business service firm (while doing the same job), while nonoutsourced workers remain at the same job and the same employer in t=-1 and t=0. We then restrict the sample to observations where the individual worker remains at the same job as in t=-1 and t=0. Thus we can indirectly control for job fixed effects and job characteristics Z jt, since we are simply comparing wages before and after outsourcing at the same job. 16 We thus isolate clear variation in employer identity that is uncorrelated with changes in the workplace or with individual productivity, yielding credible estimates of δ, the effect of outsourcing. In-house outsourcing provides a clean identification strategy for δ, but there are several caveats. First of all, in-house outsourcing is relatively rare and may not be representative of the bulk of outsourcing. There is concern about external validity, since for example in-house outsourcing may be more common among larger, more successful companies who might be paying higher wages which may lead to larger wage losses after outsourcing and thus to an overestimate for δ for the general population. On the other hand, wages after in-house outsourcing events may still be constrained by wage setting mechanisms at the outsourcing firms. For example, there might be agreements between outsourcing companies and work councils that limit wage or employment cuts after outsourcing. While in-house outsourcing thus produces estimates with high internal validity, it would be helpful to have broader estimates of the effects of outsourcing as well. For this reason, we also implement the method used by Dube and Kaplan (2010), estimating wage differences between workers in food, cleaning and security occupations who are employed at business service firms and those who are directly employed by non-business service firms using workers who, throughout their career, switch between different types of employers. These estimates have the advantage that they are based on a very large number of outsourced workers and 16 In our main specification we are assuming an individual stays at the same job as long as they do not change employers (except for the actual outsourcing event). As a robustness check we also restricted the sample further to observations where individuals do not change their occupation relative to t=-1 and found virtually identical results. 17

19 not restricted to workers who were affected by particular outsourcing events. This higher degree of external validity comes at a cost, as there is more potential for selection into who becomes an outsourced worker. While individual fixed effects control for permanent differences between workers, it may be that workers work for business service firms after some kind of shock, such as a protracted unemployment spell associated with human capital depreciation and loss in earnings potential. This could lead to downward biases in the wage estimates. We thus view the two methods as both being important and complementary pieces. 17 Next we will focus on how we estimate the effect of in-house outsourcing, returning to the alternative estimates below. Creation of Comparison Group for In-house Outsourcing In order to measure the effect of outsourcing, we require a comparison group of workers at jobs which are not outsourced. In general workers employed at outsourced and non-outsourced jobs may differ in many ways that will make them difficult to compare. In order to obtain a comparable control group, we implement a matching algorithm. For each outsourced worker, we take the set of non-outsourced workers who worked in the same industry and occupation in the year prior to outsourcing to be our potential control group. We then estimate a probit regression of whether a worker is outsourced or not, controlling for tenure and establishment size in the year prior to outsourcing as well as wages two and three years prior to outsourcing. In addition, we restrict our sample to workers with at least 2 years of tenure at their establishment in the year prior to outsourcing. For each outsourced worker we then choose the non-outsourced worker with the closest propensity score as the comparison worker Both the in-house outsourcing and industry-occupation estimates may fail to capture the effects of outsourcing on workers who are simply laid off and replaced by a business services firm. We briefly discuss this type of outsourcing, which we call Occupational Layoff outsourcing, and the effect on workers in the appendix. 18 We tested other matching specifications and found essentially the same results. For example, we matched on other variables such as current wage, full-time status, and education. We also implemented a twostep matching procedure, where we first found a control establishment for each outsourcing establishment, matching on establishment size and mean wage; in the second step, we matched each outsourced worker 18

20 Table 2 shows worker characteristics for our analysis sample as well as for a randomly selected group of workers. 19 The characteristics of the matched outsourced and non-outsourced workers are quite similar, even for characteristics that were not part of the matching algorithm, such as fulltime status and education. The random sample of non-outsourced workers tends to be younger, with higher wages and more education, than our matched group. Table 1, columns (1) and (3), provide summary statistics for the establishments that employ the OS and matched non-os workers in the year prior to outsourcing. The matched non-outsourced workers come from establishments that tend to be smaller than the establishments of outsourced workers, but otherwise the two groups are quite similar. Event Study Method To study the effect of outsourcing on job outcomes we use an event-study framework, using the full histories of our treatment and controls groups. We estimate regression models of the form: y jt = γ 0 + γ 1 I(os) + 10 k= 10 δ k I(t = t + k)i(os) + α t + θ i + ξ j + ε jt (3) where y jt is an outcome variable and I(os) is an indicator for whether job j was outsourced in year t. Each coefficient δ k measures the change in the outcome variable y jt for outsourced jobs relative to the non-outsourced control group in the k-th year before or after outsourcing occurred. α t are year fixed effects to control for year-level shocks that could affect all workers and jobs, x it are individual-level worker controls, ξ j are job fixed effects, and ε jt is an error term. Alternatively to matching one can also use all workers in the potential control group for comparison, and adjust the estimates using standard regression methods controlling for to a worker in the non-outsourcing matched establishment, matching on wage and education. This latter procedure makes it harder to find very similar individual matches in the second step, but the estimation results are very similar. 19 The random non-os workers were restricted to be age and not in either the outsourced or the matched non-outsourced groups. 19

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